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[Objective] The Grand Canal is a complex hydraulic project in the eastern China. This paper studies the flooding and its detrimental impacts in the lowland areas of the canal. [Method] The research was conducted in the Pichang-Tanxin Plain, using multi-source remote sensing data and ground surveys to analyze flooding events. We calculated the normalized difference water body index (NDWI) and the normalized difference vegetation index (NDVI) before and after flooding to assess flood severity, impact on crop growth, as well as the efficacy of hydraulic facilities in mitigating flooding effects. [Result] The NDVI difference between 2004 and 2005 for crops in flooded and non-flooded areas was 0.5 and 0.25, respectively, indicating floodings detrimentally affected crop growth and development. NDWI analysis from 2003 to 2017 demonstrated that the Taierzhuang Control Gate project mitigated flooding impacts on nearby farmlands. Flooding also affected crops in non-inundated regions, with lowland crops more adversely impacted than those outside lowlands. The hydraulic facilities significantly reduced economic losses caused by flooding. [Conclusion] Remote sensing is an effective tool for monitoring inundated areas and assessing the consequences of flooding on crops. The construction of hydraulic facilities plays a crucial role in reducing the detrimental effects of flooding in lowland regions of the Grand Canal. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To solve the large proportion of small targets and complex background in UAV (unmanned aerial vehicle) aerial image, the current object detection model has the problems of low accuracy and missed detection of small targets. Based on the YOLOv8s model, this paper proposes a 3D multi-layer feature collaboration UAV remote sensing object detection algorithm. Firstly, based on the coordinate attention, this paper proposes 3D multi-branch coordinate attention (MBCA), which improves the global feature extraction ability of the model and reduces the computation of spatial dimension by increasing the information interaction of channel dimension and the splitting and fusion of extended branches. Secondly, SPD-Conv is used to replace part of the standard convolution, which effectively retains more feature information and speeds up inference during downsampling. Then, a more efficient FastDBB_Bottleneck module is used in the C2f module, combining PConv and DBB structure reparameterization superposition to further reduce the calculation of the model. Finally, PG-Detect detection head is introduced to significantly reduce the calculation and effectively reduce the missed detection rate of small targets. Experimental results on the VisDrone2019 dataset show that the mAP50 value of the proposed method reaches 44.5%, which is 5.7 percentage points higher than that of the YOLOv8s baseline model. Simultaneously, the crack detection verification experiment is carried out on the self-built dam crack dataset, and the mAP50 value of the improved method is 3.3 percentage points higher than that of YOLOv8s, the FPS reaches 289 frames. Experimental results show that the proposed method improves the accuracy and real- time performance of the detection model in complex scene object detection, and has good adaptability and robustness. [ABSTRACT FROM AUTHOR]

As a fundamental computer vision task, image dehazing aims to preprocess degraded images by restoring color contrast and texture information to improve visibility and image quality, thereby the clear images can be recovered for subsequent high- level visual tasks, such as object detection, tracking, and object segmentation. In recent years, neural network-based dehazing methods have achieved notable success, with a growing number of Transformerbased dehazing approaches being proposed. Up to now, there is a lack of comprehensive review that thoroughly analyzes Transformer-based image dehazing algorithms. To fill this gap, this paper comprehensively sorts out Transformerbased daytime, nighttime and remote sensing image dehazing algorithms, which not only covers the fundamental principles of various types of dehazing algorithms, but also explores the applicability and performance of these algorithms in different scenarios. In addition, the commonly used datasets and evaluation metrics in image dehazing tasks are introduced. On this basis, analysis of the performance of existing representative dehazing algorithms is car- generried out from both quantitative and qualitative perspectives, and the performance of typical dehazing algorithms in terms of dehazing effect, operation speed, resource consumption is compared. Finally, the application scenarios of image dehazing technology are summarized, and the challenges and future development directions in the field of image dehazing are analyzed and prospected. [ABSTRACT FROM AUTHOR]

Water body extraction from high-resolution remote sensing images often results in incomplete and discontinuous extraction results due to factors such as complex types of ground objects and narrow rivers. To address these challenges comprehensively, combined with the spectral and texture characteristics of the water body itself, this paper proposed an innovative method for water body extraction and optimization in high-resolution remote sensing images that combines global features and local interconnection. The proposed method integrates the intrinsic spectral and textural features of water bodies, adopting a strategy of initial extraction followed by optimization and skillfully blending global and local communication features. Firstly, linear tributaries are recognized based on the initial water extraction results using multiscale Frangi filtering and the OTSU segmentation algorithm and river replenishment is applied to these linear features, resulting in preliminary optimization results. Then, a locally interconnected disconnection continuation algorithm is introduced to connect disjointed segments in the preliminary optimization results ingeniously. The final stage involves the extraction of water body segments using K-means clustering, and topology inspection and spectral inspection are performed with the accurate optimization results to achieve verification and screening of blocky water bodies. Then, rigorous checks are conducted to validate and filter block-shaped water bodies, ensuring the precision of the extraction results. Experimental results validate the outstanding performance of this method, particularly in the extraction of small tributaries, resulting in a substantial improvement in optimization accuracy. Introducing the disconnection continuation algorithm enhances the completeness of river extraction and fortifies the overall robustness of water body extraction. Comparative analysis with other methods reveals an overall accuracy improvement of 1.04% and 1.50%, along with an increase in comprehensive value by 5.84% and 8.28%, respectively. It can be used as a means to effectively improve the post-processing of water body optimization and improve the integrity and accuracy of water body extraction. [ABSTRACT FROM AUTHOR]

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Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agricultural Big Data is the property of Journal of Agricultural Big Data Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Significance] Oil crops play a significant role in the food supply, as well as the important source of edible vegetable oils and plant proteins. Real-time, dynamic and large-scale monitoring of oil crop growth is essential in guiding agricultural production, stabilizing markets, and maintaining health. Previous studies have made a considerable progress in the yield simulation of staple crops in regional scale based on remote sensing methods, but the yield simulation of oil crops in regional scale is still poor as its complexity of the plant traits and structural characteristics. Therefore, it is urgently needed to study regional oil crop yield estimation based on remote sensing technology. [Progress] This paper summarized the content of remote sensing technology in oil crop monitoring from three aspects: backgrounds, progressions, opportunities and challenges. Firstly, significances and advantages of using remote sensing technology to estimate the of oil crops have been expounded. It is pointed out that both parameter inversion and crop area monitoring were the vital components of yield estimation. Secondly, the current situation of oil crop monitoring was summarized based on remote sensing technology from three aspects of remote sensing parameter inversion, crop area monitoring and yield estimation. For parameter inversion, it is specified that optical remote sensors were used more than other sensors in oil crops inversion in previous studies. Then, advantages and disadvantages of the empirical model and physical model inversion methods were analyzed. In addition, advantages and disadvantages of optical and microwave data were further illustrated from the aspect of oil crops structure and traits characteristics. At last, optimal choice on the data and methods were given in oil crop parameter inversion. For crop area monitoring, this paper mainly elaborated from two parts of optical and microwave remote sensing data. Combined with the structure of oil crops and the characteristics of planting areas, the researches on area monitoring of oil crops based on different types of remote sensing data sources were reviewed, including the advantages and limitations of different data sources in area monitoring. Then, two yield estimation methods were introduced: remote sensing yield estimation and data assimilation yield estimation. The phenological period of oil crop yield estimation, remote sensing data source and modeling method were summarized. Next, data assimilation technology was introduced, and it was proposed that data assimilation technology has great potential in oil crop yield estimation, and the assimilation research of oil crops was ex‐ pounded from the aspects of assimilation method and grid selection. All of them indicate that data assimilation technology could improve the accuracy of regional yield estimation of oil crops. Thirdly, this paper pointed out the opportunities of remote sensing technology in oil crop monitoring, put forward some problems and challenges in crop feature selection, spatial scale determination and remote sensing data source selection of oil crop yield, and forecasted the development trend of oil crop yield estimation research in the future. [Conclusions and Prospects] The paper puts forward the following suggestions for the three aspects: (1) Regarding crop feature selection, when estimating yields for oil crops such as rapeseed and soybeans, which have active photosynthesis in siliques or pods, relying solely on canopy leaf area index (LAI) as the assimilation state variable for crop yield estimation may result in significant underestimation of yields, thereby impacting the accuracy of regional crop yield simulation. Therefore, it is necessary to consider the crop plant characteristics and the agronomic mechanism of yield formation through siliques or pods when estimating yields for oil crops. (2) In determining the spatial scale, some oil crops are distributed in hilly and mountainous areas with mixed land cover. Using regularized yield simulation grids may result in the confusion of numerous background objects, introducing additional errors and affecting the assimilation accuracy of yield estimation. This poses a challenge to yield estimation research. Thus, it is necessary to choose appropriate methods to divide irregular unit grids and determine the optimal scale for yield estimation, thereby improving the accuracy of yield estimation. (3) In terms of remote sensing data selection, the monitoring of oil crops can be influenced by crop structure and meteorological conditions. Depending solely on spectral data monitoring may have a certain impact on yield estimation results. It is important to incorporate radar off-nadir remote sensing measurement techniques to perceive the response relationship between crop leaves and siliques or pods and remote sensing data parameters. This can bridge the gap between crop characteristics and remote sensing information for crop yield simulation. This paper can serve as a valuable reference and stimulus for further research on regional yield estima‐ tion and growth monitoring of oil crops. It supplements existing knowledge and provides insightful considerations for enhancing the accuracy and efficiency of oil crop production monitoring and management. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Zhejiang University (Science Edition) is the property of Journal of Zhejiang University (Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objective] Soil salinization is a dynamic process, affected by numerous factors. Understanding its spatiotemporal variation in a region and catchment is important for management. In this paper, we present the soil salinity dynamics and its determinants in Songnen Plain of Heilongjiang province. [Method] The calculation was based on remote sensing imagery of the region acquired from 2000-2020. We used correlation analysis and relative contribution rate analysis to map the spatiotemporal distribution of soil salinity in this region. [Result] On average, areas affected by salt in the region had decreased from 2000 to 2020, at a rate of 2.73 km²/a. But this is mainly due to the reduced soil salinization in some regions, as other regions actually had seen an increase in soil salinization. In particular, soil salinization in Qiqihar had increased because of the increase in cropped lands and GDP. Suihua also saw soil salinity rising due to increased plant areas and population. In contrast, soil salinity in Daqing and Harbin had decreased, due to the increase in surface water resources and decrease in evaporation for Daqing, and the change in cropped land and livestock industry for Harbin. [Conclusion] Change in soil salinization from 2000 to 2020 in Songnen Plain varied, with some areas seeing an increase and others witnessing a decline. In addition to climate change, other reasons underlying such variations in soil salinization include economic development, change in cropping systems and livestock industry. To ameliorate soil salinization and improve land quality, this region should consider improving irrigation and drainage facilities and controlling the groundwater depth not exceeding a critical level. [ABSTRACT FROM AUTHOR]

[Objective] Understanding planting structure and crop growth in a region is important to assess its food supply and security. The objective of this paper is to investigate the feasibility of a decision-tree model derived from the Sentinel-2 remote sensing imagery to map cropping structure in fragmented irrigation regions. [Method] The study site was Alagou irrigation area in Xinjiang. The planting areas of major crops in 2021 were estimated using the Sentinel-2 remote sensing imagery. We then compared these with both field investigation and visual interpretation from the Google HD images. Critical growth stage for identifying each crop was determined based on the phenological information and the NDVI time series, from which we derived a decision tree classification model. Accuracy of the model was verified against ground-truth data. [Result] The planting structure mapped from the Sentinel-2 remote sensing imagery had sharp textures, meeting the requirements for agricultural water management. The decision tree classification model can accurately classify crops at the scale required for irrigation management. The model is simple and feasible. Compared with ground-truth data, its average accuracy is 81.56% and the Kappa coefficient is 0.716 6. [Conclusion] The Sentinel-2 remote sensing imagery and the decision tree classification method derived form it can be used to accurately identify planting structure in fragmented lands. They can provide support information for decision-making in water management, and improve agricultural water usage in irrigation districts. [ABSTRACT FROM AUTHOR]

[Objective] Change in topsoil water content is intricately linked to plant transpiration, and understanding its spatiotemporal variation at large scales is crucial to improving water and agriculture management. The objective of this paper is to investigate the feasibility of remote sensing for estimating topsoil water content, its association with crop water stress index (CWSI) and the impact of cropping systems. [Method] The experiment site was an irrigation field at Jiefangzha in Hetao Irrigation District. The topsoil water content in different cropped lands (sunflower, summer maize, spring wheat and pepper) was calculated, inversely, in 2014 using the MOD16A2 imagery, meteorological data, and the P-M model and CWSI. The estimated soil water content was compared with ground-truth data. [Result] The CWSI and topsoil water content were inversely proportional. The method was accurate for estimating topsoil water content in wheat and sunflower lands, with the determination coefficient between the estimated and measured soil water content for the wheat and sunflower lands being 0.748 and 0.357, respectively. However, it was less accurate for maize and pepper lands as they were irrigated more frequently. These suggested that the remote sensing-based method works better when the soil is dry than when it is wet. [Conclusion] The CWSI-based remote sensing method is more suitable for estimating topsoil water content in lands planted with drought-tolerant crops than lands grown with water-demanded crops which need more irrigation. Our results provide insight into the impact of cropping systems on accuracy and reliability of remote sensing methods for estimating topsoil water content. It has potential application for agricultural management in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The traditional nonnegative matrix factorization methods are based on single-layer model. The deep nonnegative matrix factorization methods are based on the mathematical theory, ignoring the actual spectral mixing process of materials. This paper started from the physical process of spectral mixing, combining the advantages of traditional nonnegative matrix factorization models and deep learning. Aiming at the problem that the deep nonnegative matrix factorization methods ignore the actual spectral mixing process of materials, the spectral mixing process is reversed from the physical spectral mixing process. Furthermore, considering the sparsity and spatial smoothness of the distribution of the endmembers in the two-dimensional space, the total variation and sparsity regularized deep nonnegative matrix factorization based on the endmember matrix is proposed. This paper verified the effectiveness of the proposed method through synthetic experiments and real data experiments, which is compared with five classical hyperspectral unmixing methods. Compared with SDNMF-TV, SAD and RMSE are reduced. The results both in the synthetic experiments and the real experiments shows that the method in this paper achieves the best unmixing results. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agricultural Big Data is the property of Journal of Agricultural Big Data Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Dynamic monitoring and quantitative estimation of forage crop growth are of great importance to the large-scale pro‐ duction of forage crop. UAV remote sensing has the advantages of high resolution, strong flexibility and low cost. In recent years, it has developed rapidly in the field of forage crop growth monitoring. In order to clarify the development status of forage crop growth monitoring and find the development direction, first, methods of UAV crop remote sensing monitoring were briefly described from two aspects of data acquisition and processing. Second, three key technologies of forage crop including canopy information extraction, spectral feature optimization and forage biomass estimation were described. Then the development trend of related research in recent years was analyzed, and it was pointed out that the number of papers published on UAV remote sensing forage crop monitoring showed an overall trend of rapidly increasing. With the rapid development of computer information technology and remote sensing technology, the application potential of UAV in the field of forage crop monitoring has been fully explored. Then, the research progress of UAV remote sensing in forage crop growth monitoring was described in five parts according to sensor types, i.e., visible, multispectral, hyperspectral, thermal infrared and LiDAR, and the research of each type of sensor were summarized and reviewed, pointing out that the current researches of hyperspectral, thermal infrared and LiDAR sensors in forage crop monitoring were less than that of visible and multispectral sensors. Finally, the future development directions were clarified according to the key technical problems that have not been solved in the research and application of UAV re‐ mote sensing forage crop growth monitoring: (1) Build a multi-temporal growth monitoring model based on the characteristics of different growth stages and different growth years of forage crops, carry out UAV remote sensing monitoring of forage crops around representative research areas to further improve the scope of application of the model. (2) Establish a multi-source data‐ base of UAV remote sensing, and carry out integrated collaborative monitoring combined with satellite remote sensing data, historical yield, soil conductivity and other data. (3) Develop an intelligent and user-friendly UAV remote sensing data analysis system, and shorten the data processing time through 5G communication network and edge computing devices. This paper could provide relevant technical references and directional guidelines for researchers in the field of forage crops and further promote the application and development of precision agriculture technology. [ABSTRACT FROM AUTHOR]

(Background and objective) Global warming and anthropogenic activities will change ecological functions of lakes, resulting in eutrophication and algal blooms, for example. Monitoring the change in aquatic vegetation and algae is important to improve lake management but difficult, especially at large scale. Remot sensing can plug this gap and in this paper, we study the feasibility of using it to acquire vegetation dynamics in lakes. (Method) The studied site is Ulansuhai lake in the upper reach of the Yellow River basin. Emergent and submerged vegetation as well as floating algae were calculated based on spectral feature analysis of the spectral data acquired from the Landsat8 imageries. A decision tree classification model was constructed to analyze the aquatic vegetation and algal blooms. A MNDSI spectral index was proposed to identify the aquatic vegetation and yellow moss algal blooms; a NDWI spectral water index was used to extract water bodies that were free of vegetation and algae. An improved enhanced vegetation index (AEVI) was used to extract the emergent vegetation; a modified macroalgal index (MFAI) was used to distinguish the submerged vegetation from the yellow moss algal blooms. The accuracy of the method was tested against ground-truth data. (Result) The spectral index combined with the decision tree classification model is effective to distinguish the aquatic vegetation from the yellow moss algal blooms. Using the Landsat8 imageries acquired from 1986 to 2018 revealed that the emergent vegetation taxa grew slowly, and the coverage area of the submerged vegetation showed periodic changes, decreasing 1986 to 2013 and increasing after 2013. The yellow moss algal bloom did not show identifiable trends, with their outbreaks occurring irregularly from 1986 to 2018. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Water use efficiency (WUE) is an important basis for ecological environment protection and reconstruction in irrigated areas. In this paper, nonlinear least squares, coefficient of variation, correlation coefficient, Mann-Kendall test, Morlet wavelet analysis and heuristic segmentation algorithm are used to study the characteristics of WUE in Qingtongxia Irrigation District of Ningxia from 1985 to 2015. The results are as follows: A The average annual WUE in the irrigated area is 0.96 g/kg, and the spatial distribution of WUE show strong spatial heterogeneity, with the characteristics of high in the west and low in the east. There is an increasing trend of fluctuation year by year, and the interannual variation of WUE has an obvious negative correlation with ET, and a significant positive correlation with TMP. © The annual variation of WUE in the irrigation area is positively correlated with ET, NPP, TMP and PRE, and the positive correlation coefficient of TMP is higher. © In general, due to a large number of ecological restoration and reconstruction projects in Qingtongxia Irrigated Area of Ningxia, the productivity of the ecosystem in the irrigated area has been improved, and the ecological status of the irrigated area has been improved to some extent. In the future, the WUE of the ecosystem in the irrigated area will continue to rise. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

SignificanceOil crops play a significant role in the food supply, as well as the important source of edible vegetable oils and plant proteins. Real-time, dynamic and large-scale monitoring of oil crop growth is essential in guiding agricultural production, stabilizing markets, and maintaining health. Previous studies have made a considerable progress in the yield simulation of staple crops in regional scale based on remote sensing methods, but the yield simulation of oil crops in regional scale is still poor as its complexity of the plant traits and structural characteristics. Therefore, it is urgently needed to study regional oil crop yield estimation based on remote sensing technology.ProgressThis paper summarized the content of remote sensing technology in oil crop monitoring from three aspects: backgrounds, progressions, opportunities and challenges. Firstly, significances and advantages of using remote sensing technology to estimate the of oil crops have been expounded. It is pointed out that both parameter inversion and crop area monitoring were the vital components of yield estimation. Secondly, the current situation of oil crop monitoring was summarized based on remote sensing technology from three aspects of remote sensing parameter inversion, crop area monitoring and yield estimation. For parameter inversion, it is specified that optical remote sensors were used more than other sensors in oil crops inversion in previous studies. Then, advantages and disadvantages of the empirical model and physical model inversion methods were analyzed. In addition, advantages and disadvantages of optical and microwave data were further illustrated from the aspect of oil crops structure and traits characteristics. At last, optimal choice on the data and methods were given in oil crop parameter inversion. For crop area monitoring, this paper mainly elaborated from two parts of optical and microwave remote sensing data. Combined with the structure of oil crops and the characteristics of planting areas, the researches on area monitoring of oil crops based on different types of remote sensing data sources were reviewed, including the advantages and limitations of different data sources in area monitoring. Then, two yield estimation methods were introduced: remote sensing yield estimation and data assimilation yield estimation. The phenological period of oil crop yield estimation, remote sensing data source and modeling method were summarized. Next, data assimilation technology was introduced, and it was proposed that data assimilation technology has great potential in oil crop yield estimation, and the assimilation research of oil crops was expounded from the aspects of assimilation method and grid selection. All of them indicate that data assimilation technology could improve the accuracy of regional yield estimation of oil crops. Thirdly, this paper pointed out the opportunities of remote sensing technology in oil crop monitoring, put forward some problems and challenges in crop feature selection, spatial scale determination and remote sensing data source selection of oil crop yield, and forecasted the development trend of oil crop yield estimation research in the future.Conclusions and ProspectsThe paper puts forward the following suggestions for the three aspects: (1) Regarding crop feature selection, when estimating yields for oil crops such as rapeseed and soybeans, which have active photosynthesis in siliques or pods, relying solely on canopy leaf area index (LAI) as the assimilation state variable for crop yield estimation may result in significant underestimation of yields, thereby impacting the accuracy of regional crop yield simulation. Therefore, it is necessary to consider the crop plant characteristics and the agronomic mechanism of yield formation through siliques or pods when estimating yields for oil crops. (2) In determining the spatial scale, some oil crops are distributed in hilly and mountainous areas with mixed land cover. Using regularized yield simulation grids may result in the confusion of numerous background objects, introducing additional errors and affecting the assimilation accuracy of yield estimation. This poses a challenge to yield estimation research. Thus, it is necessary to choose appropriate methods to divide irregular unit grids and determine the optimal scale for yield estimation, thereby improving the accuracy of yield estimation. (3) In terms of remote sensing data selection, the monitoring of oil crops can be influenced by crop structure and meteorological conditions. Depending solely on spectral data monitoring may have a certain impact on yield estimation results. It is important to incorporate radar off-nadir remote sensing measurement techniques to perceive the response relationship between crop leaves and siliques or pods and remote sensing data parameters. This can bridge the gap between crop characteristics and remote sensing information for crop yield simulation. This paper can serve as a valuable reference and stimulus for further research on regional yield estimation and growth monitoring of oil crops. It supplements existing knowledge and provides insightful considerations for enhancing the accuracy and efficiency of oil crop production monitoring and management.

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Geological Hazard & Control is the property of China Institute of Geological Environmental Monitoring (CIGEM) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Geological Hazard & Control is the property of China Institute of Geological Environmental Monitoring (CIGEM) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Unmanned Aerial Vehicle(UAV) application in agriculture and forestry has the unique advantages of high efficiency, water and pesticide saving, and strong adaptability to complex terrain. The application research of UAV in agriculture and forestry has shown a fast growing trend. In order to explore the research hotspots and the scientific impact of countries/regions and institutions on UAV application in agriculture and forestry, the relevant literatures in the Web of Science(WoS) core collection database (2011-2020) were collected. The bibliometrics analysis was performed on the journal articles of UAV application in agriculture and forestry based on VOSviewer, WoS analysis tools and Microsoft Excel. The analysis results showed that the number of published papers increased rapidly since 2017, the researches on UAV application in agriculture and forestry were carried out in 94 countries/regions, including1778 institutions. Due to the strong scientific research group in the application of UAV in agriculture and forestry of the United States, China and Australia, a large number of papers had been published, resulting in a great academic influence. Remote sensing was the most widely used technology field of UAV application in agriculture and forestry, mainly involving remote sensing technology, ecological environment science, image processing technology, geological science, etc. Engineering was an important technical field of UAV application in agriculture and forestry, mainly involving control technology, sensor technology and fluid computing modeling technology related to UAV aerial pesticide application.There were 1508 articles and reviews been published in 398 journals, about 1.90% of all journals included in WoS core collection database, indicating that more and more journals paid attention to the application research of UAV in agriculture and forestry. Remote Sensing sponsored by MDPI (Multidisciplinary Digital Publishing Institute) was the journal that published the most of papers, the most cited paper mainly focused on the research status of UAV system in photogrammetry and remote sensing, including sensing, navigation, positioning and general data processing, etc. In addition, the analysis of the research hotspots of UAV application in agriculture and forestry showed that UAV pesticide application, UAV remote sensing of diseases and pests, plant phenotype acquisition were the research hotspots. This study can provide references for innovation research and cooperation between research teams of UAV application in agriculture and forestry. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Geological Hazard & Control is the property of China Institute of Geological Environmental Monitoring (CIGEM) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The task of object detection is a basic and highly concerned work in the field of computer vision.Because object detection in remote sensing has important application value in transportation,military,agriculture,etc.,it has also become a major research hotspot.Compared with natural images,remote sensing images are affected by many factors such as complex background interference,weather,irregularities,and small objects.It is extremely challenging to achieve higher accuracy in remote sensing image object detection tasks.This paper proposes a novel object detection network based on Transformer,swin pyramid Transformer(SPT).SPT uses a sliding window Transformer module as the backbone of feature extraction.Among it,the self-attention mechanism of Transformer is very effective for detecting objects in a chaotic background,and the sliding window mode efficiently avoids a large number of square-level complexity calculations.After obtaining the feature map extracted by the backbone network,SPT uses a pyramid architecture to fuse different scale and semantic features,pithily reducing the loss of information between feature layers,and capturing the inherent multi-scale hierarchical relationship.In addition,this paper proposes self-mixed Transformer(SMT) module and cross-layer Transformer(CLT) module.SMT re-renders the highest-level feature map to enhance object feature recognition and expression.According to the feature context interaction,the feature expressions of the pixels of each feature layer are rearranged by CLT,and the CLT module is integrated into the bottom-up and top-down dual paths of the pyramid to make full use of global and local information containing different semantics.Our SPT network model is trained and tested on the UCAS-AOD and RSOD datasets.Experimental results show that SPT is high-performing in remote sensing image object detection tasks,especially suitable for irregular and small target categories,such as overpass and car.

Dynamic monitoring and quantitative estimation of forage crop growth are of great importance to the large-scale production of forage crop. UAV remote sensing has the advantages of high resolution, strong flexibility and low cost. In recent years, it has developed rapidly in the field of forage crop growth monitoring. In order to clarify the development status of forage crop growth monitoring and find the development direction, first, methods of UAV crop remote sensing monitoring were briefly described from two aspects of data acquisition and processing. Second, three key technologies of forage crop including canopy information extraction, spectral feature optimization and forage biomass estimation were described. Then the development trend of related research in recent years was analyzed, and it was pointed out that the number of papers published on UAV remote sensing forage crop monitoring showed an overall trend of rapidly increasing. With the rapid development of computer information technology and remote sensing technology, the application potential of UAV in the field of forage crop monitoring has been fully explored. Then, the research progress of UAV remote sensing in forage crop growth monitoring was described in five parts according to sensor types, i.e., visible, multispectral, hyperspectral, thermal infrared and LiDAR, and the research of each type of sensor were summarized and reviewed, pointing out that the current researches of hyperspectral, thermal infrared and LiDAR sensors in forage crop monitoring were less than that of visible and multispectral sensors. Finally, the future development directions were clarified according to the key technical problems that have not been solved in the research and application of UAV remote sensing forage crop growth monitoring: (1) Build a multi-temporal growth monitoring model based on the characteristics of different growth stages and different growth years of forage crops, carry out UAV remote sensing monitoring of forage crops around representative research areas to further improve the scope of application of the model. (2) Establish a multi-source database of UAV remote sensing, and carry out integrated collaborative monitoring combined with satellite remote sensing data, historical yield, soil conductivity and other data. (3) Develop an intelligent and user-friendly UAV remote sensing data analysis system, and shorten the data processing time through 5G communication network and edge computing devices. This paper could provide relevant technical references and directional guidelines for researchers in the field of forage crops and further promote the application and development of precision agriculture technology.

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the lack of paired datasets in the field of remote sensing image super-resolution reconstruction,current methods obtain low resolution images by bicubic interpolation,in which the degradation model is too idealized,resulting in unsatisfied reconstruction results in real low resolution remote sensing images situations.This paper proposes a super resolution reconstruction algorithm for real remote sensing images.For datasets that lack paired images,this paper builds a more reasonable degradation model,in which a prior of degradation in the imaging process (like blur,noise,down sampling,etc.) is randomly shuffled to generate realistic low-resolution images for training,simulating the generation process of low-resolution remote sensing images.Also,this paper improves a reconstruction algorithm based on generative adversarial networks(GAN) to enhance texture details by introducing attention mechanism.Experiments on UC Merced dataset show a promotion of 1.407 1 dB/0.067 2,0.821 1 dB/0.023 5 compared with ESRGAN and RCAN on the evaluation index of PSNR/SSIM,experiments on Alsat2B dataset promote 1.758 4 dB/0.048 5 compared with the baseline,which show the effective of the degradation model and reconstruction architecture.

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Crop planting structure extraction based on remote sensing images has been widely used in practice, in which the selection of classification features and samples are the key factors affecting the extraction accuracy. In order to explore the influence of different classification features and samples on the extraction accuracy of crop planting structure, with the study area in Zhangye Irrigation Area in Gansu Province, this paper uses support vector machine supervised classification method of studying the extraction accuracy of crop planting structure of spectral and temporal NDVI classification features under different samples. The results show that: 1 With the increase in the number of samples, the accuracy of the spatial distribution of the identified crop planting structure gradually increase to a stable state. 2 The average error of corn area extracted by temporal NDVI is 2.82%, the average overall classification accuracy is 84.8%, and the average Kappa coefficient is 0.81. The accuracy of crop planting structure based on temporal NDVI feature extraction is better than that of spectral feature extraction. 3 When the number of samples per 10km² in the study area is 3~4, the samples can maintain the best training effect. The research results can provide an important reference for improving the extraction accuracy of crop planting structure. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle(UAV) application in agriculture and forestry has the unique advantages of high efficiency, water and pesticide saving, and strong adaptability to complex terrain. The application research of UAV in agriculture and forestry has shown a fast growing trend. In order to explore the research hotspots and the scientific impact of countries/regions and institutions on UAV application in agriculture and forestry, the relevant literatures in the Web of Science(WoS) core collection database (2011-2020) were collected. The bibliometrics analysis was performed on the journal articles of UAV application in agriculture and forestry based on VOSviewer, WoS analysis tools and Microsoft Excel. The analysis results showed that the number of published papers increased rapidly since 2017, the researches on UAV application in agriculture and forestry were carried out in 94 countries/regions, including1778 institutions. Due to the strong scientific research group in the application of UAV in agriculture and forestry of the United States, China and Australia, a large number of papers had been published, resulting in a great academic influence. Remote sensing was the most widely used technology field of UAV application in agriculture and forestry, mainly involving remote sensing technology, ecological environment science, image processing technology, geological science, etc. Engineering was an important technical field of UAV application in agriculture and forestry, mainly involving control technology, sensor technology and fluid computing modeling technology related to UAV aerial pesticide application.There were 1508 articles and reviews been published in 398 journals, about 1.90% of all journals included in WoS core collection database, indicating that more and more journals paid attention to the application research of UAV in agriculture and forestry. Remote Sensing sponsored by MDPI (Multidisciplinary Digital Publishing Institute) was the journal that published the most of papers, the most cited paper mainly focused on the research status of UAV system in photogrammetry and remote sensing, including sensing, navigation, positioning and general data processing, etc. In addition, the analysis of the research hotspots of UAV application in agriculture and forestry showed that UAV pesticide application, UAV remote sensing of diseases and pests, plant phenotype acquisition were the research hotspots. This study can provide references for innovation research and cooperation between research teams of UAV application in agriculture and forestry.

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Plant income insurance has become an important part of agricultural insurance in China. It has been recommended to pilot since 2016 by Chinese government in several counties, and is now (2022) required to be implemented in all major grain producing counties in the 13 major grain producing provinces. The measurement of yield for plant income insurance in such huge volume urgently needs the support of remote sensing technology. Therefore, the development history and application status of remote sensing technology in the whole agricultural insurance industry was reviewed to help understanding the whole context circumstances of plant income insurance firstly. Then, the application scenarios of remote sensing technology were analyzed, and the key remote sensing technologies involved were introduced. The technologies involved include crop field plot extraction, crop classification, crop disaster estimation, and crop yield estimation. Research progress of these technologies were reviewed and summarized,and the satellite data sources that most commonly used in plant income insurance were summarized as well. It was found that to obtain a better support for a development of plant income insurance as well as all crop insurance from remote sensing communities, issues existed not only in the involved remote sensing technologies, but also in the remote sensing industry as well as the insurance industry. The most two important technical problems in the current application scenario of planting income insurance are that: the plot extraction and crop classification are not automated enough; the yield estimation mechanism is not strong, and the accuracy is not high. At the industry level, the first issue is the limitation of the remote sensing technology itself in that the remote sensing is not almighty, suffering from limited data source, either from satellite or from other platform, laborious data preprocessing, and pricey data fees for most of the data, and the second is the compatibility between the current business of the insurance industry and the combination of remote sensing. In this regard, this paper proposed in total five specific suggestions, which are: 1st, to establish a data distribution platform to solve the problems of difficult data acquisition and processing and standardization of initial data; 2nd, to improve the sample database to promote the automation of plot extraction and crop classification; 3rd, to achieve faster, more accurate and more scientific yields through multidisciplinary research; 4th, to standardize remote sensing technology application in agricultural insurance, and 5th, to write remote sensing applications in crop insurance contract. With these improvements, the application mode of plant income insurance and probably the whole agriculture insurance would run in a way with easily available data, more automated and intelligent technology, standards to follow, and contract endorsements. [ABSTRACT FROM AUTHOR]

For the first time, the proposed railway from China to Nepal will cross the Himalayan region, where glacial lakes are most concentrated. The calculation of the parameters needed for bridge clearance setting and pier protection and the characteristic value of ice lake outburst for linear engineering have not been found in domestic and foreign standards and codes. In this paper, the reserves and distribution characteristics of the source materials are delineated by means of geological survey, multi - historical satellite remote sensing images from 1984 to the present, SBAS - InSAR differential interferogram deformation analysis, rain - flood method, RAMMS numerical simulation, etc. The change trend of reservoir capacity and areas of Guorucuo Glacial Lake are analyzed, and the structure and stability of germinal moraine and trailing edge glacier are evaluated. The results of rain-flood method are as follows: velocity 3. 51 m/ s, mud depth 1. 26 m, overall impact force 29. 30 kN/ m2 ; RAMMS calculation: velocity 3. 54 m/ s, mud depth 1. 28 m, overall impact force 19. 00 kN/ m2 . Comparing the results of rain-flood method and RAMMS numerical simulation, the results of rain-flood method and numerical simulation are close to each other in the section of railway bridge. Therefore, it is suggested that 7. 89 m should be adopted for the clearance of railway bridge on the ice lake outburst passage. This method is used to study how to calculate the characteristic value of ice-lake outburst debris flow in the linear engineering of ice-lake developing area. [ABSTRACT FROM AUTHOR]

Objective The Gouli area is one of the important gold and polymetallic belt in the East Kunlun Orogen. There is no research on lineament identification and mineral exploration based on multisource remote sensing data in the area. Methods In this paper, various enhancement techniques were employed on Landsat 8 OLI, GF-2 and DEM data to delineate structural lineaments. Meanwhile, information on geological, mineral and geochemical anomalies was combined to define prospecting targets in the Gouli area. Results The results show that (1) The tectonic framework of the study area can be divided into two regions by the boundary of Kaketeer-Seride. The lineament in the northern region is most developed in the NNE direction, followed by the near-EW direction, and less distributed in the NW and NE directions. The lineament in the southern region is mainly developed in the near-EW direction, and the NW to NE directions are less distributed. (2) The gold ore is controlled by lineaments. (3) The areas of densely distributed and intersected lineaments, as well as the areas connected by different colors on remote sensing images, are favorable locations for gold mineralization. Finally, based on the comprehensive analysis of multiple geological information and remote sensing interpreted tectonic information, four prospecting targets were identified in the study area and verified in the field. Conclusion This study proves that multisource remote sensing data can better identify the spatial structure characteristics of surface tectonics, which can provide a reference and basis for prospecting new deposits in the Gouli area.

Copyright of Journal of Agricultural Big Data is the property of Journal of Agricultural Big Data Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)